Perbandingan Performa Algoritma Metode Bagging dan Boosting pada Prediksi Konsentrasi PM10 di Jakarta Utara

Elita Rizkiani Putri(1*), Dede Brahma Arianto(2)
(1) Program Studi Kesehatan Lingkungan, Fakultas Kesehatan Masyarakat, Universitas Indonesia
(2) Magister Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia
(*) Corresponding Author



Abstrak


Jakarta Utara merupakan salah satu wilayah di DKI Jakarta yang mengalami peningkatan hari dengan kualitas udara berkategori tidak sehat, yakni 21 hari pada tahun 2017 menjadi 117 hari di 2018, tetapi kemudian menurun menjadi 45 hari pada tahun 2019. Kategori tidak sehat tersebut dipengaruhi oleh polusi udara. Salah satu polutan yang ada di udara adalah PM10. Saat ini, kualitas udara dapat diprediksi menggunakan pendekatan algoritma machine learning. Contoh metode machine learning yang terkenal adalah Metode Bagging dan Boosting yang ada di Metode Ensemble. Contoh algoritma dengan Metode Bagging adalah Random Forest, sedangkan pada Metode Boosting adalah Catboost dan XGBoost. Penelitian ini bertujuan membandingkan performa algoritma Metode Bagging berupa Random Forest dan algoritma Metode Boosting berupa Catboost dan XGBoost dalam memprediksi konsentrasi PM10 di Jakarta Utara. Data yang digunakan adalah data harian tahun 2017—2019 untuk faktor meteorologis dan polutan lainnya di wilayah tersebut. Faktor meteorologis digunakan karena faktor ini dapat memengaruhi konsentrasi dan pembentukan polutan. Sementara itu, faktor polutan digunakan karena beberapa penelitian sebelumnya menggunakan faktor ini dalam memprediksi konsentrasi PM10. Penelitian ini dilakukan dengan studi literatur, pemerolehan data, pra-pemprosesan data, dan pemodelan data. Beberapa metrik evaluasi juga digunakan untuk melihat evaluasi dari pemodelan. Berdasarkan hasil pemodelan, algoritma Random Forest menghasilkan akurasi data testing yang lebih tinggi (R2 = 0,6424) dibandingkan XGBoost (R2 = 0,6340) dan Catboost (R2 = 0,6294).

Kata Kunci


PM10, Faktor meteorologi, Random Forest, Catboost, XGBoost


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Referensi


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